{"id":"W2325178406","doi":"10.1109/cjece.2015.2492919","title":"Low-Complexity PAPR Reduction Technique for OFDM Systems Using Biased Subcarriers","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Subcarrier; Orthogonal frequency-division multiplexing; Reduction (mathematics); Transmitter; Algorithm; Cumulative distribution function; Bit error rate; Computer science; Mathematics; Subcarrier multiplexing; Multiplexing; Function (biology); Electronic engineering; Statistics; Telecommunications; Probability density function; Channel (broadcasting); Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001612527,0.0001476266,0.0002307036,0.0004119845,0.00006444772,0.00005327728,0.0001163967,0.00009227249,0.000003693428],"category_scores_gemma":[0.0000392814,0.0001238471,0.00007566612,0.0002346468,0.00003305979,0.000167029,0.000004307376,0.0001590859,4.338058e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003628594,"about_ca_system_score_gemma":0.0001304871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007890807,"about_ca_topic_score_gemma":0.000007497184,"domain_scores_codex":[0.9991212,0.00001354295,0.0003242958,0.0001084321,0.00008455524,0.0003479498],"domain_scores_gemma":[0.9992352,0.00006027537,0.00005618394,0.00008681198,0.0001047292,0.0004567882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002924057,0.00001451328,0.0002846177,0.0003686752,0.000238478,0.00007054488,0.0001372057,0.3679849,0.6060199,0.004152115,0.002992891,0.01770703],"study_design_scores_gemma":[0.001516039,0.0004465753,0.001198423,0.0009415951,0.00009123125,0.004542523,0.00001890337,0.8894928,0.08794757,0.0006027291,0.01226051,0.0009411204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1705387,0.0005646444,0.8272915,0.00004589428,0.001332163,0.00016154,0.000008155581,0.00004645692,0.00001085721],"genre_scores_gemma":[0.9886171,0.00001975926,0.01047202,0.000005731807,0.0008338985,0.000009406822,6.128262e-7,0.0000330685,0.000008355014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8180784,"threshold_uncertainty_score":0.5050338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01781824017913166,"score_gpt":0.1976118380636475,"score_spread":0.1797935978845158,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}